High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

SED-MVS		78.97 1	84.56 1	72.45 1	81.70 2	86.20 2	77.82 3	59.97 5	88.89 1	65.96 1	86.00 5	84.02 1	70.03 1	76.19 4	76.17 5	79.22 17	94.46 1

DVP-MVS		77.54 2	84.41 2	69.54 6	79.93 3	86.08 3	77.20 8	60.31 3	88.62 2	62.54 2	86.67 3	83.77 2	58.04 32	75.84 7	75.69 8	79.21 18	94.17 2
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
SF-MVS		76.41 3	80.45 6	71.69 2	82.90 1	86.54 1	82.08 1	64.58 1	81.67 11	59.82 5	86.26 4	77.90 8	61.11 15	71.81 27	70.75 34	79.63 11	88.22 23

MSP-MVS		76.38 4	82.99 3	68.68 7	71.93 17	78.65 22	77.61 5	55.44 18	88.04 3	60.25 4	92.24 1	77.08 10	69.84 2	75.48 8	75.69 8	76.99 56	93.75 3
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DVP-MVS++		75.99 5	81.32 5	69.77 5	71.86 19	85.13 4	77.62 4	59.87 7	82.69 10	61.55 3	83.05 9	79.63 6	69.78 3	76.01 5	75.89 6	77.92 38	86.86 35

DPE-MVS		75.74 6	82.82 4	67.49 11	77.07 6	82.01 7	77.05 9	57.70 11	86.55 5	55.44 15	90.50 2	82.52 3	60.33 19	72.99 15	72.98 16	77.33 47	92.19 6
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DPM-MVS		74.63 7	78.53 11	70.07 3	76.10 8	82.56 6	79.30 2	59.89 6	80.49 13	57.75 10	66.98 25	76.16 13	65.95 4	79.35 1	78.47 1	81.45 5	85.71 44

APDe-MVS		74.59 8	80.23 7	68.01 10	76.51 7	80.20 14	77.39 6	58.18 9	85.31 6	56.84 12	84.89 6	76.08 14	60.66 17	71.85 26	71.76 21	78.47 27	91.49 9

MCST-MVS		74.06 9	77.71 14	69.79 4	78.95 4	81.99 8	76.33 10	62.16 2	75.89 19	52.96 23	64.37 30	73.30 21	65.66 5	77.49 2	77.43 3	82.67 1	93.51 4

CNVR-MVS		73.87 10	78.60 10	68.35 9	73.32 12	81.97 9	76.19 11	59.29 8	80.12 14	56.70 13	67.09 24	76.48 11	64.26 7	75.88 6	75.75 7	80.32 7	92.93 5

SMA-MVS		73.31 11	79.53 8	66.05 13	71.25 20	80.13 15	74.99 12	56.09 14	84.14 7	54.48 17	73.74 16	80.23 4	61.43 12	74.96 9	74.09 12	78.08 35	89.42 13
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
CSCG		72.98 12	76.86 16	68.46 8	78.23 5	81.74 10	77.26 7	60.00 4	75.61 22	59.06 6	62.72 32	77.42 9	56.63 44	74.24 11	77.18 4	79.56 12	89.13 17

HPM-MVS++		72.44 13	78.73 9	65.11 14	71.88 18	77.31 32	71.98 20	55.67 16	83.11 9	53.59 21	75.90 12	78.49 7	61.00 16	73.99 12	73.31 15	76.55 60	88.97 18

APD-MVS		71.86 14	77.91 13	64.80 16	70.39 24	75.69 42	74.02 14	56.14 13	83.59 8	52.92 24	84.67 7	73.46 20	59.30 25	69.47 43	69.66 43	76.02 67	88.84 19
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_NAP		71.50 15	77.27 15	64.77 17	69.64 26	79.26 16	73.53 15	54.73 24	79.32 16	54.23 18	74.81 13	74.61 18	59.40 24	73.00 14	72.17 19	77.10 55	87.72 27

NCCC		71.36 16	75.44 18	66.60 12	72.46 15	79.18 18	74.16 13	57.83 10	76.93 17	54.19 19	63.47 31	71.08 25	61.30 14	73.56 13	73.70 13	79.69 10	90.19 10

train_agg		70.74 17	76.53 17	63.98 19	70.33 25	75.16 46	72.33 19	55.78 15	75.74 20	50.41 32	80.08 11	73.15 22	57.75 36	71.96 25	70.94 31	77.25 51	88.69 21

TSAR-MVS + MP.		70.28 18	75.09 19	64.66 18	69.34 28	64.61 130	72.60 18	56.29 12	80.73 12	58.36 8	84.56 8	75.22 16	55.37 51	69.11 49	69.45 44	75.97 69	81.97 76
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DeepPCF-MVS	62.48 1	70.07 19	78.36 12	60.39 41	62.38 58	76.96 35	65.54 56	52.23 32	87.46 4	49.07 33	74.05 15	76.19 12	59.01 27	72.79 19	71.61 23	74.13 109	89.49 12

SteuartSystems-ACMMP		69.78 20	74.76 20	63.98 19	73.45 11	78.56 23	73.13 17	55.24 21	70.68 32	48.93 35	70.43 20	69.10 27	54.00 57	72.78 21	72.98 16	79.14 19	88.74 20
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		68.75 21	72.84 22	63.98 19	68.87 32	75.09 47	71.87 21	51.22 36	73.50 26	58.17 9	68.05 23	68.67 28	57.79 35	70.49 36	69.23 46	75.98 68	84.84 54

SD-MVS		68.30 22	72.58 24	63.31 24	69.24 29	67.85 103	70.81 26	53.65 29	79.64 15	58.52 7	74.31 14	75.37 15	53.52 63	65.63 73	63.56 108	74.13 109	81.73 81
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
DELS-MVS		67.36 23	70.34 37	63.89 22	69.12 30	81.55 11	70.82 25	55.02 22	53.38 75	48.83 36	56.45 46	59.35 55	60.05 22	74.93 10	74.78 10	79.51 13	91.95 7
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
MP-MVS		67.34 24	73.08 21	60.64 38	66.20 37	76.62 37	69.22 32	50.92 38	70.07 33	48.81 37	69.66 21	70.12 26	53.68 60	68.41 54	69.13 48	74.98 88	87.53 29
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DeepC-MVS	60.65 2	67.33 25	71.52 31	62.44 27	59.79 78	74.84 49	68.89 33	55.56 17	73.91 25	53.50 22	55.00 52	65.63 33	60.08 21	71.99 24	71.33 27	76.85 57	87.94 26
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HQP-MVS		67.22 26	72.08 26	61.56 33	66.76 35	73.58 58	71.41 22	52.98 30	69.92 35	43.85 59	70.58 19	58.75 57	56.76 42	72.90 17	71.88 20	77.57 43	86.94 34

CANet		67.21 27	71.83 28	61.83 29	64.51 43	79.25 17	66.72 49	48.73 54	68.49 40	50.63 31	61.40 36	66.47 31	61.44 11	69.31 47	69.90 39	78.94 23	88.00 24

CDPH-MVS		67.03 28	71.64 29	61.65 32	69.10 31	76.84 36	71.35 24	55.42 19	67.02 43	42.83 64	65.27 29	64.60 37	53.16 66	69.70 42	71.40 25	78.02 37	86.67 36

MAR-MVS		66.85 29	69.81 38	63.39 23	73.56 10	80.51 13	69.87 28	51.51 35	67.78 42	46.44 45	51.09 66	61.60 50	60.38 18	72.67 22	73.61 14	78.59 24	81.44 85
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
DeepC-MVS_fast	60.18 3	66.84 30	70.69 35	62.36 28	62.76 52	73.21 61	67.96 36	52.31 31	72.26 29	51.03 26	56.50 45	64.26 38	63.37 8	71.64 28	70.85 32	76.70 59	86.10 41
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		66.77 31	72.21 25	60.44 40	61.23 66	70.00 84	64.26 60	47.79 67	72.98 27	56.32 14	71.35 18	72.33 23	55.68 50	65.49 74	66.66 70	77.35 45	86.62 37

MVS_0304		66.31 32	71.61 30	60.14 43	62.59 56	78.98 20	67.13 45	45.75 94	64.35 48	45.23 53	60.69 38	67.67 30	61.73 10	71.09 31	71.03 29	78.41 31	87.44 30

ACMMPR		66.20 33	71.51 32	60.00 45	65.34 41	74.04 53	69.39 30	50.92 38	71.97 30	46.04 47	66.79 26	65.68 32	53.07 67	68.93 51	69.12 49	75.21 82	84.05 60

3Dnovator	58.39 4	65.97 34	66.85 52	64.94 15	73.72 9	79.03 19	67.73 39	54.25 25	61.52 51	52.79 25	42.27 92	60.73 53	62.01 9	71.29 29	71.75 22	79.12 20	81.34 88

TSAR-MVS + ACMM		65.95 35	72.83 23	57.93 55	69.35 27	65.85 122	73.36 16	39.84 148	76.00 18	48.69 38	82.54 10	75.03 17	49.38 95	65.33 76	63.42 110	66.94 169	81.67 82

canonicalmvs		65.55 36	70.75 34	59.49 49	62.11 61	78.26 27	66.52 50	43.82 118	71.54 31	47.84 40	61.30 37	61.68 48	58.48 30	67.56 61	69.67 42	78.16 34	85.25 50

QAPM		65.47 37	67.82 45	62.72 26	72.56 13	81.17 12	67.43 42	55.38 20	56.07 68	43.29 62	43.60 87	65.38 35	59.10 26	72.20 23	70.76 33	78.56 25	85.59 47

PGM-MVS		65.35 38	70.43 36	59.43 50	65.78 39	73.75 55	69.41 29	48.18 63	68.80 39	45.37 51	65.88 28	64.04 39	52.68 74	68.94 50	68.68 54	75.18 83	82.93 67

PHI-MVS		65.17 39	72.07 27	57.11 65	63.02 50	77.35 31	67.04 46	48.14 65	68.03 41	37.56 90	66.00 27	65.39 34	53.19 65	70.68 33	70.57 36	73.72 117	86.46 40

CLD-MVS		64.69 40	67.25 47	61.69 31	68.22 34	78.33 25	63.09 64	47.59 70	69.64 36	53.98 20	54.87 53	53.94 73	57.87 33	72.79 19	71.34 26	79.40 15	69.87 157
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS_111021_HR		64.66 41	67.11 50	61.80 30	71.04 21	77.91 28	62.75 67	54.78 23	51.43 78	47.54 41	53.77 56	54.85 70	56.84 40	70.59 34	71.50 24	77.86 39	89.70 11

EPNet		64.39 42	70.93 33	56.77 67	60.58 73	75.77 39	59.28 88	50.58 42	69.93 34	40.73 79	68.59 22	61.60 50	53.72 58	68.65 52	68.07 56	75.75 74	83.87 62
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVS		64.37 43	69.48 39	58.39 52	62.21 60	71.81 76	67.27 43	49.51 48	69.40 38	45.76 49	60.41 39	64.96 36	51.84 76	67.33 65	67.57 63	73.78 116	84.89 52

DROMVSNet		64.30 44	68.19 41	59.76 47	62.97 51	75.31 45	67.26 44	44.19 112	60.73 54	47.52 42	55.84 48	62.12 46	57.67 37	70.71 32	67.47 64	78.97 22	85.13 51

casdiffmvs_mvg		64.26 45	67.60 46	60.36 42	62.26 59	78.54 24	69.39 30	48.33 61	56.54 63	45.36 52	52.86 60	57.36 62	58.42 31	70.28 37	70.24 38	78.43 28	87.39 32
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvs		63.87 46	67.08 51	60.12 44	60.90 69	78.29 26	67.91 37	48.01 66	55.89 70	44.97 54	50.45 68	56.94 63	59.54 23	70.17 40	69.81 40	79.41 14	87.99 25
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_Test		63.75 47	67.24 48	59.68 48	60.01 74	76.99 34	68.13 35	45.17 98	57.45 62	43.74 60	53.07 59	56.16 68	61.33 13	70.27 38	71.11 28	79.72 9	85.63 46

X-MVS		63.53 48	68.62 40	57.60 59	64.77 42	73.06 62	65.82 54	50.53 43	65.77 45	42.02 72	58.20 43	63.42 42	47.83 106	68.25 58	68.50 55	74.61 98	83.16 66

ACMMP		63.27 49	67.85 44	57.93 55	62.64 55	72.30 73	68.23 34	48.77 53	66.50 44	43.05 63	62.07 33	57.84 60	49.98 87	66.58 69	66.46 76	74.93 89	83.17 64
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
CS-MVS		63.16 50	68.01 43	57.49 60	57.39 94	72.73 67	63.38 63	45.16 99	59.37 56	46.49 44	58.93 42	57.68 61	56.31 45	71.12 30	70.37 37	76.23 66	85.88 42

ETV-MVS		62.88 51	68.18 42	56.70 68	58.47 86	74.89 48	60.26 80	43.96 115	58.27 61	42.37 70	61.47 35	56.56 64	57.80 34	68.00 59	68.74 52	77.34 46	89.33 16

AdaColmap		62.79 52	62.63 68	62.98 25	70.82 22	72.90 65	67.84 38	54.09 27	65.14 46	50.71 29	41.78 94	47.64 101	60.17 20	67.41 64	66.83 68	74.28 104	76.69 112

3Dnovator+	55.76 7	62.70 53	65.10 60	59.90 46	65.89 38	72.15 74	62.94 66	49.82 47	62.77 50	49.06 34	43.62 86	61.47 52	58.60 29	68.51 53	66.75 69	73.08 131	80.40 96

OpenMVS	55.62 8	62.57 54	63.76 65	61.19 35	72.13 16	78.84 21	64.42 58	50.51 44	56.44 65	45.67 50	36.88 123	56.51 65	56.66 43	68.28 57	68.96 50	77.73 41	80.44 95

PVSNet_BlendedMVS		62.53 55	66.37 54	58.05 53	58.17 87	75.70 40	61.30 73	48.67 57	58.67 57	50.93 27	55.43 50	49.39 90	53.01 69	69.46 44	66.55 73	76.24 64	89.39 14

PVSNet_Blended		62.53 55	66.37 54	58.05 53	58.17 87	75.70 40	61.30 73	48.67 57	58.67 57	50.93 27	55.43 50	49.39 90	53.01 69	69.46 44	66.55 73	76.24 64	89.39 14

MVSTER		62.51 57	67.22 49	57.02 66	55.05 113	69.23 92	63.02 65	46.88 81	61.11 53	43.95 58	59.20 41	58.86 56	56.80 41	69.13 48	70.98 30	76.41 62	82.04 73

CHOSEN 1792x2688		62.48 58	64.06 64	60.64 38	72.50 14	84.18 5	62.43 68	53.77 28	47.90 92	39.85 83	25.15 186	44.76 115	53.72 58	77.29 3	77.61 2	81.60 4	91.53 8

CostFormer		62.45 59	65.68 58	58.67 51	63.29 47	77.65 29	67.62 40	38.42 158	54.04 73	46.00 48	48.27 76	57.89 59	56.97 39	67.03 67	67.79 62	79.74 8	87.09 33

PCF-MVS	55.99 6	62.31 60	66.60 53	57.32 63	59.12 85	73.68 57	67.53 41	48.71 55	61.35 52	42.83 64	51.33 65	63.48 41	53.48 64	65.64 72	64.87 92	72.22 136	85.83 43
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
diffmvs		62.30 61	66.05 56	57.92 57	57.08 95	75.60 44	66.90 47	47.06 79	55.45 72	43.37 61	53.45 58	55.60 69	57.21 38	66.57 70	68.00 58	75.89 72	87.70 28
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DI_MVS_plusplus_trai		61.86 62	65.26 59	57.90 58	57.93 91	74.51 51	66.30 51	46.49 87	49.96 82	41.62 75	42.69 90	61.77 47	58.74 28	70.25 39	69.32 45	76.31 63	88.30 22

MSLP-MVS++		61.81 63	62.19 73	61.37 34	68.33 33	63.08 144	70.75 27	38.89 154	63.96 49	57.51 11	48.59 74	61.66 49	53.67 61	62.04 118	59.92 153	79.03 21	76.08 115

CS-MVS-test		61.68 64	65.97 57	56.67 69	57.77 92	72.59 70	57.63 95	45.54 96	58.53 60	47.11 43	59.45 40	56.34 66	55.15 52	64.52 86	65.03 90	76.80 58	85.34 49

OPM-MVS		61.59 65	62.30 72	60.76 37	66.53 36	73.35 60	71.41 22	54.18 26	40.82 122	41.57 76	45.70 82	54.84 71	54.43 56	69.92 41	69.19 47	76.45 61	82.25 70
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MS-PatchMatch		61.41 66	61.88 76	60.85 36	70.57 23	75.98 38	66.29 52	46.91 80	50.56 80	48.28 39	36.30 126	51.64 77	50.95 82	72.89 18	70.65 35	82.13 3	75.17 122

EIA-MVS		60.56 67	64.29 63	56.20 74	59.14 84	72.68 69	59.55 86	43.56 121	51.78 77	41.01 78	55.47 49	51.93 76	55.87 47	65.01 80	66.57 72	78.06 36	86.60 39

ACMP	56.21 5	59.78 68	61.81 78	57.41 62	61.15 67	68.88 94	65.98 53	48.85 52	58.56 59	44.19 57	48.89 72	46.31 107	48.56 100	63.61 101	64.49 100	75.75 74	81.91 77
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LGP-MVS_train		59.69 69	62.59 69	56.31 72	61.94 62	68.15 100	66.90 47	48.15 64	59.75 55	38.47 86	50.38 69	48.34 98	46.87 111	65.39 75	64.93 91	75.51 78	81.21 90

Effi-MVS+		59.63 70	61.78 79	57.12 64	61.56 63	71.63 77	63.61 61	47.59 70	47.18 93	37.79 87	45.29 83	49.93 86	56.27 46	67.45 62	67.06 66	75.91 70	83.93 61

CPTT-MVS		59.54 71	64.47 62	53.79 84	54.99 115	67.63 106	65.48 57	44.59 106	64.81 47	37.74 88	51.55 63	59.90 54	49.77 91	61.83 120	61.26 138	70.18 150	84.31 59

baseline2		59.20 72	61.72 80	56.27 73	59.61 80	74.12 52	58.65 91	49.42 49	48.10 90	40.12 82	49.10 71	44.15 117	51.24 79	66.65 68	67.88 61	78.56 25	82.06 72

GeoE		58.97 73	60.94 81	56.67 69	61.27 65	72.71 68	61.35 72	45.69 95	49.19 86	41.22 77	39.55 110	49.58 89	52.79 73	64.79 82	65.89 80	77.73 41	84.87 53

baseline		58.65 74	61.99 74	54.75 79	54.70 117	71.85 75	60.20 81	43.91 116	55.99 69	40.13 81	53.50 57	50.91 83	55.76 48	61.29 128	61.73 130	73.83 113	78.68 104

PVSNet_Blended_VisFu		58.56 75	62.33 71	54.16 81	56.90 96	73.92 54	57.72 94	46.16 92	44.23 100	42.73 67	46.26 79	51.06 82	46.28 114	67.99 60	65.38 85	75.18 83	87.44 30

ACMM	53.73 9	57.91 76	58.27 96	57.49 60	63.10 48	66.45 116	65.65 55	49.02 51	53.69 74	42.67 68	36.41 125	46.07 110	50.38 85	64.74 84	64.63 97	74.14 108	75.91 116
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CANet_DTU		57.87 77	63.63 66	51.15 99	52.18 124	70.20 83	58.14 93	37.32 165	56.49 64	31.06 122	57.38 44	50.05 85	53.67 61	64.98 81	65.04 89	74.57 99	81.29 89

ET-MVSNet_ETH3D		57.84 78	61.91 75	53.09 87	32.91 205	74.53 50	63.51 62	46.80 83	46.52 95	36.14 96	56.00 47	46.20 108	64.41 6	60.75 136	66.99 67	74.79 90	82.35 68

tpm cat1		57.41 79	58.26 97	56.42 71	60.80 71	72.56 71	64.35 59	38.43 157	49.18 87	46.36 46	36.69 124	43.50 121	54.47 54	61.39 126	62.64 118	74.11 111	81.81 78

IB-MVS	53.15 10	57.33 80	59.02 88	55.37 76	60.83 70	77.11 33	54.51 119	50.10 46	43.22 106	42.82 66	40.50 100	37.61 141	44.67 126	59.27 150	69.81 40	79.29 16	85.59 47
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
tpmrst		57.23 81	59.08 87	55.06 77	59.91 76	70.65 81	60.71 76	35.38 176	47.91 91	42.58 69	39.78 105	45.45 112	54.44 55	62.19 115	62.82 115	77.37 44	84.73 55

baseline1		57.21 82	60.53 83	53.33 86	62.50 57	69.86 86	57.33 99	50.59 41	43.39 105	30.00 128	48.60 73	51.09 81	42.36 138	69.38 46	68.03 57	77.20 52	73.39 130

FA-MVS(training)		57.15 83	60.42 84	53.34 85	58.15 89	72.77 66	59.79 84	38.68 155	49.01 88	36.56 95	40.79 98	45.44 113	53.04 68	65.23 79	67.93 60	73.82 114	81.80 80

HyFIR lowres test		57.12 84	59.11 86	54.80 78	61.55 64	77.55 30	59.02 89	45.00 101	41.84 119	33.93 108	22.44 193	49.16 93	51.02 81	68.39 55	68.71 53	78.26 33	85.70 45

MVS_111021_LR		57.06 85	60.60 82	52.93 88	56.25 100	65.14 128	55.16 117	41.21 140	52.32 76	44.89 55	53.92 55	49.27 92	52.16 75	61.46 124	60.54 146	67.92 162	81.53 84

DCV-MVSNet		56.80 86	58.96 89	54.28 80	59.96 75	66.74 114	60.37 79	44.87 103	41.01 121	36.81 93	47.57 77	47.87 100	48.23 103	64.41 88	65.17 87	75.45 79	79.95 98

Anonymous20231211		56.40 87	57.00 107	55.70 75	59.78 79	72.49 72	61.29 75	46.83 82	40.50 124	40.46 80	22.12 195	49.73 87	51.07 80	64.39 89	65.30 86	74.74 92	84.44 58

PMMVS		55.74 88	62.68 67	47.64 130	44.34 174	65.58 126	47.22 157	37.96 161	56.43 66	34.11 106	61.51 34	47.41 102	54.55 53	65.88 71	62.49 122	67.67 164	79.48 99

Fast-Effi-MVS+		55.73 89	58.26 97	52.76 89	54.33 118	68.19 99	57.05 100	34.66 178	46.92 94	38.96 85	40.53 99	41.55 130	55.69 49	65.31 77	65.99 77	75.90 71	79.34 100

FC-MVSNet-train		55.68 90	57.00 107	54.13 82	63.37 45	66.16 118	46.77 160	52.14 33	42.36 113	37.67 89	48.50 75	41.42 132	51.28 78	61.58 123	63.22 112	73.56 119	75.76 119

FMVSNet3		55.66 91	59.68 85	50.96 101	50.59 138	66.49 115	57.57 96	46.61 84	49.30 83	28.77 133	39.61 106	51.42 78	43.85 131	68.29 56	68.80 51	78.35 32	73.86 125

OMC-MVS		55.48 92	61.85 77	48.04 129	41.55 181	60.32 161	56.80 104	31.78 198	75.67 21	42.30 71	51.52 64	54.15 72	49.91 89	60.28 141	57.59 160	65.91 172	73.42 128

tpm		54.94 93	57.86 102	51.54 97	59.48 82	67.04 110	58.34 92	34.60 180	41.93 118	34.41 103	42.40 91	47.14 103	49.07 98	61.46 124	61.67 134	73.31 126	83.39 63

GBi-Net		54.66 94	58.42 94	50.26 109	49.36 147	65.81 123	56.80 104	46.61 84	49.30 83	28.77 133	39.61 106	51.42 78	42.71 134	64.25 92	65.54 82	77.32 48	73.03 133

test1		54.66 94	58.42 94	50.26 109	49.36 147	65.81 123	56.80 104	46.61 84	49.30 83	28.77 133	39.61 106	51.42 78	42.71 134	64.25 92	65.54 82	77.32 48	73.03 133

test-LLR		54.62 96	58.66 92	49.89 115	51.68 130	65.89 120	47.88 151	46.35 88	42.51 110	29.84 129	41.41 95	48.87 94	45.20 119	62.91 109	64.43 101	78.43 28	84.62 56

TSAR-MVS + COLMAP		54.37 97	62.43 70	44.98 145	34.33 201	58.94 168	54.11 124	34.15 189	74.06 24	34.57 102	71.63 17	42.03 129	47.88 105	61.26 129	57.33 163	64.83 175	71.74 143

EPMVS		54.07 98	56.06 113	51.75 96	56.74 98	70.80 79	55.32 115	34.20 186	46.46 96	36.59 94	40.38 102	42.55 124	49.77 91	61.25 130	60.90 142	77.86 39	70.08 154

v2v482		54.00 99	55.12 120	52.69 91	51.73 129	69.42 91	60.65 77	45.09 100	34.56 155	33.73 111	35.29 129	35.36 151	49.92 88	64.05 98	65.16 88	75.00 87	81.98 75

CNLPA		54.00 99	57.08 106	50.40 108	49.83 144	61.75 152	53.47 127	37.27 166	74.55 23	44.85 56	33.58 141	45.42 114	52.94 72	58.89 152	53.66 182	64.06 178	71.68 144

FMVSNet2		53.94 101	57.29 104	50.03 112	49.36 147	65.81 123	56.80 104	45.95 93	43.13 107	28.04 137	35.68 127	48.18 99	42.71 134	67.23 66	67.95 59	77.32 48	73.03 133

v8		53.77 102	54.82 125	52.54 92	52.12 125	66.95 113	60.56 78	43.23 127	37.17 144	35.35 98	34.96 132	37.50 143	49.51 94	63.67 100	64.59 98	74.48 101	78.91 103

GA-MVS		53.77 102	56.41 112	50.70 103	51.63 132	69.96 85	57.55 97	44.39 107	34.31 156	27.15 139	40.99 97	36.40 147	47.65 108	67.45 62	67.16 65	75.83 73	78.60 105

Effi-MVS+-dtu		53.63 104	54.85 124	52.20 94	59.32 83	61.33 155	56.42 110	40.24 146	43.84 102	34.22 105	39.49 111	46.18 109	53.00 71	58.72 156	57.49 162	69.99 153	76.91 110

thisisatest0530		53.61 105	57.22 105	49.40 120	51.30 134	68.22 98	52.72 135	43.34 125	42.72 109	35.31 99	43.57 88	44.14 118	44.37 129	63.00 107	64.86 93	69.34 156	74.00 124

v1144		53.47 106	54.65 126	52.10 95	51.93 127	69.81 87	59.32 87	44.77 105	33.21 162	32.52 114	33.55 142	34.34 159	49.29 96	64.58 85	64.81 95	74.74 92	82.27 69

v10		53.44 107	54.40 127	52.31 93	52.08 126	66.99 111	59.68 85	43.41 122	35.90 150	34.30 104	33.98 139	35.56 149	50.10 86	64.39 89	64.67 96	74.32 102	79.30 101

PatchmatchNet		53.37 108	55.62 118	50.75 102	55.93 107	70.54 82	51.39 140	36.41 169	44.85 98	37.26 91	39.40 113	42.54 125	47.83 106	60.29 140	60.88 144	75.69 76	70.87 148
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test2506		53.36 109	57.36 103	48.68 125	55.53 109	68.11 101	54.31 121	46.25 90	43.54 103	22.21 161	40.19 103	43.69 120	36.56 151	64.15 96	65.94 78	77.20 52	75.91 116

IterMVS-LS		53.36 109	55.65 117	50.68 105	55.34 111	59.04 166	55.00 118	39.98 147	38.72 132	33.22 112	44.52 85	47.05 104	49.63 93	61.82 121	61.77 129	70.92 145	76.61 114
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		53.30 111	58.66 92	47.04 133	44.94 168	65.89 120	47.88 151	35.95 172	42.51 110	29.84 129	41.41 95	48.87 94	45.20 119	62.91 109	64.43 101	78.43 28	84.62 56

tttt0517		53.05 112	56.73 111	48.76 123	50.35 140	67.51 107	51.96 139	43.34 125	42.00 117	33.88 109	43.19 89	43.49 122	44.37 129	62.58 114	64.86 93	68.67 158	73.46 127

MDTV_nov1_ep13		52.99 113	55.59 119	49.95 114	54.08 119	70.69 80	56.47 109	38.42 158	42.78 108	30.19 127	39.56 109	43.31 123	45.78 116	60.07 145	62.11 126	74.74 92	70.62 149

EPP-MVSNet		52.91 114	58.91 90	45.91 138	54.99 115	68.84 95	49.27 146	42.71 134	37.53 138	20.20 169	46.09 80	56.19 67	36.90 149	61.37 127	60.90 142	71.41 140	81.41 86

dps		52.84 115	52.92 138	52.74 90	59.89 77	69.49 90	54.47 120	37.38 164	42.49 112	39.53 84	35.33 128	32.71 164	51.83 77	60.45 137	61.12 139	73.33 125	68.86 163

v1192		52.69 116	53.86 130	51.31 98	51.22 135	69.76 88	57.37 98	44.39 107	32.21 165	31.39 121	32.41 150	32.44 167	49.19 97	64.25 92	64.17 103	74.31 103	81.81 78

V42		52.63 117	55.08 121	49.76 117	44.93 169	67.49 109	60.19 82	42.13 137	37.21 143	34.08 107	34.57 135	37.30 144	47.29 109	63.48 103	64.15 104	69.96 154	81.38 87

MSDG		52.58 118	51.40 151	53.95 83	65.48 40	64.31 138	61.44 71	44.02 113	44.17 101	32.92 113	30.40 163	31.81 171	46.35 113	62.13 116	62.55 120	73.49 121	64.41 171

ECVR-MVS		52.52 119	55.88 115	48.60 126	55.53 109	68.11 101	54.31 121	46.25 90	43.54 103	21.75 163	32.76 147	39.83 139	36.56 151	64.15 96	65.94 78	77.20 52	76.81 111

Fast-Effi-MVS+-dtu		52.47 120	55.89 114	48.48 127	56.25 100	65.07 129	58.75 90	23.79 209	41.27 120	27.07 141	37.95 118	41.34 133	50.85 83	62.90 111	62.34 124	74.17 107	80.37 97

v144192		52.43 121	53.63 132	51.03 100	51.06 136	69.60 89	56.94 102	44.84 104	32.15 166	30.88 123	32.45 149	32.71 164	48.36 101	62.98 108	63.52 109	74.10 112	82.02 74

thres100view900		52.33 122	53.91 129	50.48 107	56.10 102	67.79 104	56.18 112	49.18 50	35.86 152	25.22 147	34.74 133	34.10 160	42.41 137	64.45 87	62.62 119	73.81 115	77.85 106

v1921920		51.95 123	53.19 134	50.51 106	50.82 137	69.14 93	55.45 114	44.34 111	31.53 170	30.53 125	31.96 152	31.67 172	48.31 102	63.12 105	63.28 111	73.59 118	81.60 83

v148		51.72 124	53.15 135	50.05 111	50.15 142	67.51 107	56.98 101	42.85 132	32.60 164	32.41 116	33.88 140	34.71 156	44.45 127	61.06 131	63.00 114	73.45 122	79.24 102

TAPA-MVS	47.92 11	51.66 125	57.88 101	44.40 148	36.46 196	58.42 171	53.82 126	30.83 199	69.51 37	34.97 101	46.90 78	49.67 88	46.99 110	58.00 159	54.64 178	63.33 184	68.00 165
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IS_MVSNet		51.53 126	57.98 100	44.01 152	55.96 106	66.16 118	47.65 153	42.84 133	39.82 127	19.09 177	44.97 84	50.28 84	27.20 184	63.43 104	63.84 105	71.33 142	77.33 108

v1240		51.42 127	52.66 140	49.97 113	50.31 141	68.70 96	54.05 125	43.85 117	30.78 174	30.22 126	31.43 156	31.03 179	47.98 104	62.62 113	63.16 113	73.40 123	80.93 92

pmmvs4		51.28 128	52.50 142	49.85 116	49.54 146	63.02 145	52.83 134	43.41 122	44.65 99	35.71 97	34.38 136	32.25 168	45.14 122	60.21 144	60.03 150	72.44 135	72.98 136

Vis-MVSNet		51.13 129	58.04 99	43.06 158	47.68 154	67.71 105	49.10 147	39.09 153	37.75 136	22.57 158	51.03 67	48.78 96	32.42 169	62.12 117	61.80 128	67.49 166	77.12 109
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UGNet		51.04 130	58.79 91	42.00 164	40.59 183	65.32 127	46.65 162	39.26 151	39.90 126	27.30 138	54.12 54	52.03 75	30.93 173	59.85 147	59.62 155	67.23 168	80.70 93
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
tfpn200view9		50.91 131	52.45 143	49.11 122	56.10 102	64.53 133	53.06 131	47.31 75	35.86 152	25.22 147	34.74 133	34.10 160	41.08 140	60.84 133	61.37 136	71.90 139	75.70 120

SCA		50.88 132	53.70 131	47.59 131	55.99 104	55.81 180	43.14 174	33.45 192	45.16 97	37.14 92	41.83 93	43.82 119	44.43 128	60.37 138	60.02 151	71.38 141	68.90 162

gg-mvs-nofinetune		50.82 133	55.83 116	44.97 146	60.63 72	75.69 42	53.40 128	34.48 182	20.05 209	6.93 204	18.27 201	52.70 74	33.57 159	70.50 35	72.93 18	80.84 6	80.68 94

thres200		50.76 134	52.52 141	48.70 124	55.98 105	64.60 131	55.29 116	47.34 73	33.91 159	24.36 150	34.33 137	33.90 162	37.27 147	60.84 133	62.41 123	71.99 137	77.63 107

test1111		50.62 135	54.98 123	45.55 141	53.84 121	68.48 97	48.99 148	47.25 76	40.60 123	15.64 185	31.51 155	38.32 140	33.01 166	64.34 91	66.62 71	74.55 100	74.95 123

thres400		50.39 136	52.22 144	48.26 128	55.02 114	66.32 117	52.97 132	48.33 61	32.68 163	22.94 156	33.21 144	33.38 163	37.27 147	62.74 112	61.38 135	73.04 132	75.81 118

EG-PatchMatch MVS		50.23 137	50.89 154	49.47 118	59.54 81	70.88 78	52.46 136	44.01 114	26.22 195	31.91 117	24.97 187	31.45 175	33.48 161	64.79 82	66.51 75	75.40 80	71.39 146

IterMVS		50.23 137	53.27 133	46.68 134	47.59 156	60.58 159	53.10 130	36.62 168	36.07 148	25.89 144	39.42 112	40.05 136	43.65 132	60.22 143	61.35 137	73.23 127	75.23 121
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet1		50.14 139	52.78 139	47.06 132	45.56 165	63.56 141	54.22 123	43.74 119	34.10 158	25.37 146	29.79 169	42.06 128	38.70 143	64.25 92	65.54 82	74.75 91	70.18 153

ACMH	47.82 13	50.10 140	49.60 160	50.69 104	63.36 46	66.99 111	56.83 103	52.13 34	31.06 173	17.74 182	28.22 175	26.24 195	45.17 121	60.88 132	63.80 106	68.91 157	70.00 156
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EPNet_dtu		49.85 141	56.99 109	41.52 167	52.79 122	57.06 174	41.44 179	43.13 128	56.13 67	19.24 176	52.11 61	48.38 97	22.14 191	58.19 158	58.38 158	70.35 148	68.71 164
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		49.59 142	49.75 159	49.40 120	55.88 108	59.86 163	56.31 111	45.33 97	48.57 89	28.32 136	31.54 154	36.81 146	46.27 115	57.17 164	55.88 173	64.29 177	58.42 189

UniMVSNet_NR-MVSNet		49.56 143	53.04 136	45.49 142	51.59 133	64.42 137	46.97 158	51.01 37	37.87 134	16.42 183	39.87 104	34.91 155	33.43 163	59.59 148	62.70 116	73.52 120	71.94 139

CDS-MVSNet		49.25 144	53.97 128	43.75 154	47.53 157	64.53 133	48.59 149	42.27 136	33.77 160	26.64 142	40.46 101	42.26 127	30.01 176	61.77 122	61.71 131	67.48 167	73.28 132
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	44.22 14	49.14 145	51.75 147	46.10 137	42.78 179	55.60 183	53.11 129	34.46 183	55.69 71	32.47 115	34.16 138	41.45 131	48.91 99	57.13 165	54.09 179	64.84 174	64.10 172
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMH+	47.85 12	49.13 146	48.86 166	49.44 119	56.75 97	62.01 151	56.62 108	47.55 72	37.49 139	23.98 151	26.68 180	29.46 186	43.12 133	57.45 163	58.85 157	68.62 159	70.05 155

NR-MVSNet		48.84 147	51.76 146	45.44 143	57.66 93	60.64 157	47.39 154	47.63 68	37.26 140	13.31 188	37.31 120	29.64 185	33.53 160	63.52 102	62.09 127	73.10 130	71.89 142

CR-MVSNet		48.82 148	51.85 145	45.29 144	46.74 159	55.95 178	52.06 137	34.21 184	42.17 114	31.74 118	32.92 146	42.53 126	45.00 123	58.80 153	61.11 140	61.99 189	69.47 158

thres600view7		48.44 149	50.23 157	46.35 136	54.05 120	64.60 131	50.18 143	47.34 73	31.73 169	20.74 167	32.28 151	32.62 166	33.79 158	60.84 133	56.11 171	71.99 137	73.40 129

test-mter		48.31 150	55.04 122	40.45 171	34.12 202	59.02 167	41.77 178	28.05 203	38.43 133	22.67 157	39.35 114	44.40 116	41.88 139	60.30 139	61.68 133	74.20 105	82.12 71

PatchT		48.11 151	51.27 153	44.43 147	50.13 143	61.58 153	33.59 192	32.92 194	40.38 125	31.74 118	30.60 162	36.93 145	45.00 123	58.80 153	61.11 140	73.19 128	69.47 158

TranMVSNet+NR-MVSNet		48.06 152	51.36 152	44.21 150	50.38 139	62.09 150	47.28 155	50.88 40	36.11 147	13.25 189	37.51 119	31.60 174	30.70 174	59.34 149	62.53 121	72.81 133	70.31 151

TransMVSNet (Re)		47.46 153	48.94 165	45.74 140	57.96 90	64.29 139	48.26 150	48.47 60	26.33 194	19.33 174	29.45 172	31.28 178	25.31 188	63.05 106	62.70 116	75.10 86	65.47 169

DU-MVS		47.33 154	50.86 155	43.20 157	44.43 172	60.64 157	46.97 158	47.63 68	37.26 140	16.42 183	37.31 120	31.39 176	33.43 163	57.53 161	59.98 152	70.35 148	71.94 139

v7n		47.22 155	48.38 167	45.87 139	48.20 153	63.58 140	50.69 141	40.93 144	26.60 193	26.44 143	26.52 181	29.65 184	38.19 145	58.22 157	60.23 149	70.79 146	73.83 126

UA-Net		47.19 156	53.02 137	40.38 172	55.31 112	60.02 162	38.41 185	38.68 155	36.42 146	22.47 160	51.95 62	58.72 58	25.62 187	54.11 177	53.40 183	61.79 190	56.51 192

Baseline_NR-MVSNet		47.14 157	50.83 156	42.84 160	44.43 172	63.31 143	44.50 170	50.36 45	37.71 137	11.25 194	30.84 159	32.09 169	30.96 172	57.53 161	63.73 107	75.53 77	70.60 150

pmmvs5		47.02 158	50.02 158	43.51 156	43.48 177	62.65 147	47.24 156	37.78 163	30.59 175	24.80 149	35.26 130	30.43 180	34.36 156	59.05 151	60.28 148	73.40 123	71.92 141

UniMVSNet (Re)		46.89 159	51.65 149	41.34 169	45.60 164	62.71 146	44.05 171	47.10 78	37.24 142	13.55 187	36.90 122	34.54 158	26.76 185	57.56 160	59.90 154	70.98 144	72.69 137

thisisatest0515		46.88 160	49.57 161	43.74 155	45.33 167	60.46 160	46.19 164	41.06 143	30.34 176	29.73 131	32.50 148	31.63 173	35.43 154	58.75 155	61.71 131	64.70 176	71.59 145

tfpnnormal		46.61 161	46.82 174	46.37 135	52.70 123	62.31 148	50.39 142	47.17 77	25.74 197	21.80 162	23.13 191	24.15 203	33.45 162	60.28 141	60.77 145	72.70 134	71.39 146

pm-mvs1		46.14 162	49.34 164	42.41 161	48.93 150	62.22 149	44.98 168	42.68 135	27.66 187	20.76 166	29.88 168	34.96 154	26.41 186	60.03 146	60.42 147	70.70 147	70.20 152

IterMVS-SCA-FT		45.87 163	51.55 150	39.24 175	46.22 160	59.43 164	52.89 133	31.93 195	36.01 149	23.68 152	38.86 115	39.88 138	39.05 142	56.25 170	58.17 159	41.70 210	72.25 138

MIMVSNet		45.62 164	49.56 162	41.02 170	38.17 187	64.43 136	49.48 145	35.43 175	36.53 145	20.06 171	22.58 192	35.16 153	28.75 181	61.97 119	62.20 125	74.20 105	64.07 173

gm-plane-assit		45.41 165	48.03 169	42.34 162	56.49 99	40.48 208	24.54 212	34.15 189	14.44 215	6.59 205	17.82 202	35.32 152	49.82 90	72.93 16	74.11 11	82.47 2	81.12 91

ADS-MVSNet		45.39 166	46.42 175	44.19 151	48.74 152	57.52 172	43.91 172	31.93 195	35.89 151	27.11 140	30.12 164	32.06 170	45.30 117	53.13 183	55.19 175	68.15 161	61.07 181

GG-mvs-BLEND		44.87 167	64.59 61	21.86 208	0.01 224	73.70 56	55.99 113	0.01 221	50.70 79	0.01 225	49.18 70	63.61 40	0.01 220	63.83 99	64.50 99	75.13 85	86.62 37

pmmvs-eth3d		44.67 168	45.27 180	43.98 153	42.56 180	55.72 182	44.97 169	40.81 145	31.96 168	29.13 132	26.09 183	25.27 200	36.69 150	55.13 174	56.62 168	69.68 155	66.12 168

MDTV_nov1_ep13_2view		44.44 169	45.75 178	42.91 159	46.13 161	63.43 142	46.53 163	34.20 186	29.08 182	19.95 172	26.23 182	27.89 190	35.88 153	53.36 182	56.43 169	74.74 92	63.86 174

CMPMVS	33.64 16	44.39 170	46.41 176	42.03 163	44.21 175	56.50 176	46.73 161	26.48 208	34.20 157	35.14 100	24.22 188	34.64 157	40.52 141	56.50 169	56.07 172	59.12 194	62.74 177
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Vis-MVSNet (Re-imp)		44.31 171	51.67 148	35.72 185	51.82 128	55.24 184	34.57 191	41.63 138	39.10 130	8.84 201	45.93 81	46.63 106	14.45 201	54.09 178	57.03 165	63.00 185	63.65 175

TAMVS		44.27 172	49.35 163	38.35 179	44.74 170	61.04 156	39.07 183	31.82 197	29.95 178	18.34 180	33.55 142	39.94 137	30.01 176	56.85 167	57.58 161	66.13 171	66.54 166

MVS-HIRNet		43.98 173	43.63 184	44.39 149	47.66 155	59.31 165	32.66 198	33.88 191	30.15 177	33.75 110	16.82 207	28.39 189	45.25 118	53.92 181	55.00 177	73.16 129	61.80 178

UniMVSNet_ETH3D		43.97 174	46.01 177	41.59 165	38.31 186	56.20 177	49.69 144	38.18 160	28.18 183	19.88 173	27.82 177	30.20 181	33.41 165	54.18 176	56.30 170	70.05 152	69.17 160

RPMNet		43.70 175	48.17 168	38.48 178	45.52 166	55.95 178	37.66 186	26.63 207	42.17 114	25.47 145	29.59 171	37.61 141	33.87 157	50.85 188	52.02 187	61.75 191	69.00 161

PatchMatch-RL		43.37 176	44.93 181	41.56 166	37.94 188	51.70 186	40.02 181	35.75 173	39.04 131	30.71 124	35.14 131	27.43 192	46.58 112	51.99 184	50.55 191	58.38 196	58.64 187

FMVSNet5		43.29 177	47.07 172	38.87 176	30.46 207	50.99 188	45.87 165	37.19 167	42.17 114	19.32 175	26.77 179	40.51 134	30.26 175	56.82 168	55.81 174	70.10 151	56.46 193

test0.0.03 1		43.07 178	46.95 173	38.54 177	51.68 130	58.77 169	35.28 187	46.35 88	32.05 167	12.44 190	28.53 174	35.52 150	14.40 202	57.12 166	56.93 166	71.11 143	59.69 183

anonymousdsp		43.03 179	47.19 171	38.18 180	36.00 198	56.92 175	38.44 184	34.56 181	24.22 199	22.53 159	29.69 170	29.92 182	35.21 155	53.96 180	58.98 156	62.32 188	76.66 113

USDC		42.80 180	45.57 179	39.58 173	34.55 200	51.13 187	42.61 175	36.21 170	39.59 128	23.65 153	33.13 145	20.87 209	37.86 146	55.35 173	57.16 164	62.61 186	61.75 179

pmnet_mix02		42.41 181	43.24 186	41.44 168	45.80 163	57.46 173	42.19 176	41.57 139	29.38 180	23.39 154	26.08 184	23.96 204	27.31 183	51.50 185	53.76 181	68.36 160	60.58 182

CHOSEN 280x420		42.39 182	47.40 170	36.54 183	33.56 203	39.66 211	40.67 180	26.88 206	34.66 154	18.03 181	30.09 165	45.59 111	44.82 125	54.46 175	54.00 180	55.28 203	73.32 131

pmmvs6		41.90 183	44.01 183	39.43 174	44.45 171	58.77 169	41.92 177	39.22 152	21.74 202	19.08 178	17.40 205	31.33 177	24.28 190	55.94 171	56.67 167	67.60 165	66.24 167

Anonymous20231206		40.63 184	43.29 185	37.53 181	48.88 151	55.81 180	34.99 188	44.98 102	28.16 184	10.16 198	17.26 206	27.50 191	18.28 195	54.00 179	55.07 176	67.85 163	65.23 170

CVMVSNet		38.91 185	44.49 182	32.40 194	34.57 199	47.20 199	34.81 189	34.20 186	31.45 171	8.95 200	38.86 115	36.38 148	24.30 189	47.77 192	46.94 203	57.59 198	62.85 176

COLMAP_ROB	34.79 15	38.65 186	40.72 189	36.23 184	36.41 197	49.22 195	45.51 167	27.60 205	37.81 135	20.54 168	23.37 190	24.25 202	28.11 182	51.02 187	48.55 194	59.22 193	50.82 203
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		38.23 187	41.72 188	34.15 187	40.56 184	50.07 191	33.17 195	44.35 110	27.64 189	5.54 211	30.84 159	26.67 193	14.99 199	45.64 195	52.38 186	66.29 170	58.83 186

WR-MVS		37.61 188	42.15 187	32.31 196	43.64 176	51.85 185	29.39 204	43.35 124	27.65 188	4.40 213	29.90 167	29.80 183	10.46 206	46.73 194	51.98 188	62.60 187	57.16 190

TinyColmap		37.18 189	37.37 202	36.95 182	31.17 206	45.21 202	39.71 182	34.65 179	29.83 179	20.20 169	18.54 200	13.72 217	38.27 144	50.33 189	51.57 189	57.71 197	52.42 200

CP-MVSNet		37.09 190	40.62 190	32.99 189	37.56 190	48.25 196	32.75 196	43.05 129	27.88 186	5.93 207	31.27 157	25.82 198	15.09 197	43.37 202	48.82 192	63.54 182	58.90 184

DTE-MVSNet		36.91 191	40.44 191	32.79 192	40.74 182	47.55 198	30.71 202	44.39 107	27.03 191	4.32 214	30.88 158	25.99 196	12.73 204	45.58 196	50.80 190	63.86 179	55.23 196

PS-CasMVS		36.84 192	40.23 194	32.89 190	37.44 191	48.09 197	32.68 197	42.97 131	27.36 190	5.89 208	30.08 166	25.48 199	14.96 200	43.28 203	48.71 193	63.39 183	58.63 188

WR-MVS_H		36.29 193	40.35 193	31.55 198	37.80 189	49.94 193	30.57 203	41.11 142	26.90 192	4.14 215	30.72 161	28.85 187	10.45 207	42.47 204	47.99 198	65.24 173	55.54 194

SixPastTwentyTwo		36.11 194	37.80 198	34.13 188	37.13 194	46.72 200	34.58 190	34.96 177	21.20 205	11.66 191	29.15 173	19.88 210	29.77 178	44.93 197	48.34 195	56.67 200	54.41 198

test20.03		36.00 195	38.92 195	32.60 193	45.92 162	50.99 188	28.05 208	43.69 120	21.62 203	6.03 206	17.61 204	25.91 197	8.34 213	51.26 186	52.60 185	63.58 180	52.46 199

TDRefinement		35.76 196	38.23 196	32.88 191	19.09 216	46.04 201	43.29 173	29.49 200	33.49 161	19.04 179	22.29 194	17.82 212	29.69 180	48.60 191	47.24 201	56.65 201	52.12 201

LTVRE_ROB	32.83 17	35.10 197	37.46 199	32.35 195	43.12 178	49.99 192	28.52 206	33.23 193	12.73 216	8.18 202	27.71 178	21.34 207	32.64 168	46.92 193	48.11 196	48.41 207	55.45 195
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
PM-MVS		34.96 198	38.17 197	31.22 199	22.78 211	40.82 207	33.56 193	23.61 210	29.16 181	21.43 165	28.00 176	21.43 206	31.90 170	44.33 200	42.12 206	54.07 205	61.34 180

testgi		34.51 199	37.42 200	31.12 200	47.37 158	50.34 190	24.38 213	41.21 140	20.32 207	5.64 210	20.56 196	26.55 194	8.06 214	49.28 190	52.65 184	60.05 192	42.23 209

MDA-MVSNet-bldmvs		34.31 200	34.11 206	34.54 186	24.73 209	49.66 194	33.42 194	43.03 130	21.59 204	11.10 195	19.81 198	12.68 218	31.41 171	35.59 209	48.05 197	63.56 181	51.39 202

N_pmnet		34.09 201	35.74 204	32.17 197	37.25 193	43.17 205	32.26 200	35.57 174	26.22 195	10.60 197	20.44 197	19.38 211	20.20 193	44.59 199	47.00 202	57.13 199	49.35 206

RPSCF		33.61 202	40.43 192	25.65 204	16.00 218	32.41 213	31.73 201	13.33 217	50.13 81	23.12 155	31.56 153	40.09 135	32.73 167	41.14 208	37.05 209	36.99 213	50.63 204

EU-MVSNet		33.00 203	36.49 203	28.92 201	33.10 204	42.86 206	29.32 205	35.99 171	22.94 200	5.83 209	25.29 185	24.43 201	15.21 196	41.22 207	41.65 208	54.08 204	57.01 191

pmmvs3		31.22 204	33.62 207	28.43 202	22.82 210	40.26 210	26.40 209	22.05 212	16.89 213	10.99 196	14.72 209	16.26 213	29.70 179	44.82 198	47.39 200	58.61 195	54.98 197

FC-MVSNet-test		30.97 205	37.38 201	23.49 207	37.42 192	33.68 212	19.43 215	39.27 150	31.37 172	1.67 221	38.56 117	28.85 187	6.06 217	41.40 205	43.80 205	37.10 212	44.03 208

new-patchmatchnet		30.47 206	32.80 209	27.75 203	36.81 195	43.98 203	24.85 211	39.29 149	20.52 206	4.06 216	15.94 208	16.05 214	9.57 208	41.32 206	42.05 207	51.94 206	49.74 205

MIMVSNet1		29.60 207	33.37 208	25.20 206	19.52 214	43.94 204	26.29 210	37.92 162	19.95 210	3.79 217	12.64 213	21.99 205	7.70 215	43.83 201	46.32 204	55.97 202	44.92 207

FPMVS		26.87 208	28.19 210	25.32 205	27.09 208	29.49 214	32.28 199	17.79 214	28.09 185	11.33 192	19.38 199	14.69 215	20.88 192	35.11 210	32.82 211	42.56 209	37.75 210

PMVS	18.18 18	21.95 209	22.85 211	20.90 209	21.92 212	14.78 216	19.95 214	17.31 215	15.69 214	11.32 193	13.70 210	13.91 216	15.02 198	34.92 211	31.72 212	39.85 211	35.20 211
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		19.10 210	22.71 212	14.89 211	10.93 220	24.08 215	14.22 216	13.94 216	18.68 211	2.93 218	12.84 212	11.27 219	11.94 205	30.57 213	30.58 213	35.38 214	30.93 212

Gipuma		17.16 211	17.83 213	16.36 210	18.76 217	12.15 219	11.97 217	27.78 204	17.94 212	4.86 212	2.53 220	2.73 224	8.90 211	34.32 212	36.09 210	25.92 215	19.06 215
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method		13.92 212	17.14 214	10.16 214	1.69 223	6.92 222	11.25 218	5.74 218	22.41 201	8.11 203	10.40 214	20.91 208	13.73 203	22.17 214	13.98 216	20.44 216	23.18 213

PMMVS2		12.25 213	14.17 215	10.00 215	11.39 219	14.35 217	8.21 219	19.29 213	9.31 217	0.19 224	7.38 216	6.19 222	1.10 219	19.26 215	21.13 215	19.85 217	21.56 214

E-PMN		10.66 214	8.30 217	13.42 212	19.91 213	7.87 220	4.30 222	29.47 201	8.37 220	1.70 220	3.67 217	1.29 227	9.12 210	8.98 219	13.59 217	16.03 218	14.30 218

EMVS		10.15 215	7.67 218	13.05 213	19.22 215	7.77 221	4.48 220	29.34 202	8.65 219	1.67 221	3.55 218	1.36 226	9.15 209	8.15 220	11.79 219	14.44 219	12.43 219

MVE	10.35 19	9.76 216	11.08 216	8.22 216	4.43 221	13.04 218	3.36 223	23.57 211	5.74 221	1.76 219	3.09 219	1.75 225	6.78 216	12.78 217	23.04 214	9.44 220	18.09 216
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.01 217	0.01 219	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.01 222	0.00 226	0.02 221	0.00 228	0.00 222	0.01 221	0.01 220	0.00 223	0.03 220

test123		0.01 217	0.01 219	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.01 222	0.00 226	0.02 221	0.00 228	0.01 220	0.00 222	0.01 220	0.00 223	0.03 220

uanet_test		0.00 219	0.00 221	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 224	0.00 226	0.00 223	0.00 228	0.00 222	0.00 222	0.00 222	0.00 223	0.00 222

sosnet-low-res		0.00 219	0.00 221	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 224	0.00 226	0.00 223	0.00 228	0.00 222	0.00 222	0.00 222	0.00 223	0.00 222

sosnet		0.00 219	0.00 221	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 224	0.00 226	0.00 223	0.00 228	0.00 222	0.00 222	0.00 222	0.00 223	0.00 222

RE-MVS-def										21.59 164

9.14												80.07 5

SR-MVS					63.74 44			48.51 59				73.80 19

Anonymous202405211			56.81 110		60.91 68	73.48 59	59.82 83	48.68 56	39.26 129		24.00 189	46.77 105	50.73 84	65.28 78	65.72 81	75.37 81	83.17 64

our_test_3					49.68 145	61.50 154	45.84 166

ambc			35.52 205		38.36 185	40.40 209	28.38 207		25.20 198	14.87 186	13.22 211	7.54 221	19.34 194	55.63 172	47.79 199	47.91 208	58.89 185

MTAPA										54.82 16		71.98 24

MTMP										50.64 30		68.31 29

Patchmatch-RL test							0.69 225

tmp_tt				4.41 217	2.56 222	1.81 224	2.61 224	0.27 220	20.12 208	9.81 199	17.69 203	9.04 220	1.96 218	12.88 216	12.11 218	9.23 221

XVS					62.70 53	73.06 62	61.80 69			42.02 72		63.42 42				74.68 96

X-MVStestdata					62.70 53	73.06 62	61.80 69			42.02 72		63.42 42				74.68 96

mPP-MVS					63.08 49							62.34 45

NP-MVS									72.62 28

Patchmtry						64.49 135	52.06 137	34.21 184		31.74 118

DeepMVS_CX						5.87 223	4.32 221	1.74 219	9.04 218	1.30 223	7.97 215	3.16 223	8.56 212	9.74 218		6.30 222	14.51 217

High-resolution multi-view benchmark